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ai-optimizer

ai-optimizer

Local
github.com/shivam-app-developersDeveloper Tools0Updated 21w ago

About ai-optimizer

AI Token Optimizer is an MCP server for coding agents that filters framework-related noise from file, search and diagnostic results. It applies detected project packs and ignore rules, including .gitignore, to reduce context sent to models.

Key features

  1. 01Framework-aware file skipping and line slices
  2. 02Filtered file listing and content search
  3. 03LSP diagnostics with focused code windows
  4. 04Symbol lookup through LSP
  5. 05Clean terminal output and track token savings

Use cases

  1. 01Avoid loading generated or irrelevant files
  2. 02Search a project while respecting ignore rules
  3. 03Inspect diagnostics with a narrow code excerpt
  4. 04Check detected frameworks and cumulative savings

MCP support

The MCP server provides optimized file, directory, search, diagnostic and status tools for MCP-speaking coding agents.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"shivam-app-developers-ai-optimizer": {
"command": "npx",
"args": [
"-y",
"@ai-optimizer/core"
],
"env": {
"AI_OPTIMIZER_LICENSE": "<AI_OPTIMIZER_LICENSE>",
"AI_OPTIMIZER_LSP": "<AI_OPTIMIZER_LSP>",
"AI_OPTIMIZER_TELEMETRY": "<AI_OPTIMIZER_TELEMETRY>"
}
}
}
}
Run commandnpx -y @ai-optimizer/core
Environment variables to set
AI_OPTIMIZER_LICENSESecretEd25519 JWT license key. Unlocks Pro/Team packs; free tier needs no key.
AI_OPTIMIZER_LSPSet to 0 to disable the LSP bridge (optimized_diagnostics).
AI_OPTIMIZER_TELEMETRYSet to 'on' to enable anonymous opt-in telemetry. Off by default.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@ai-optimizer/core0.1.0

Tools

11

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
pathstringYesPath to the file, relative to the project root
endLineintegerNo1-indexed end line (inclusive)
startLineintegerNo1-indexed start line (inclusive)

Details

From the project README

AI Token Optimizer

An MCP server that cuts AI coding-agent token usage by 60–80% via framework-aware context optimization.

CI CodeQL npm License: MIT

AI coding agents read whole files when they need 15 lines. They load *.g.dart, R.java, node_modules/, and dist/ into context — then do it again next turn. AI Token Optimizer is a Model Context Protocol server that intercepts those tool calls and strips framework noise before it reaches the model.

Works with anything that speaks MCP: Claude Code, Cursor, Cline, Continue, Zed, JetBrains AI, GitHub Copilot agent mode, Antigravity, Windsurf, OpenAI Codex CLI.

Quickstart

npm install -g @ai-optimizer/core

Then point your agent at the ai-optimizer stdio server. For Claude Code, add to ~/.claude/settings.json:

{
  "mcpServers": {
    "ai-optimizer": {
      "command": "ai-optimizer"
    }
  }
}

Or scaffold the config and preview your savings automatically:

npx @ai-optimizer/init
# "we'd skip 47 files, ~14K tokens/session, ~$0.42/session at Sonnet pricing"

Full per-agent setup (Cursor, others), LSP install, and verification steps live in packages/core/README.md.

What it does

It registers tools the agent uses instead of its built-in file/dir/error reads:

ToolWhat it saves
optimized_read_fileFramework-aware skip + optional line slice
optimized_list_filesWalks the tree applying ignore globs (+ .gitignore)
optimized_grepContent search scoped by ignore filters
optimized_diagnosticsSpawns the right LSP, returns errors + a narrow code window
read_symbolLSP workspace/symbol slice instead of a full-file read (Pro/LSP)
strip_bash_noiseStrips ANSI, npm/maven/gradle progress, JVM warnings
optimizer_statusDetected frameworks, active packs, cumulative tokens saved

When a project type is detected (e.g. Python via pyproject.toml), the matching pack's ignore rules activate automatically. .gitignore is respected on top.

Tiers

Free (MIT core)Pro ($9/mo)Team ($29/seat)
Framework packsPython, JS/TS+ React, Flutter, Java, Kotlin, Go, Rust, C#, Swift, Ruby, Elixir, PHP, Solidityall Pro packs
History compaction—✓✓
Scheduler (claude -p cron)—✓+ work-stealing across providers
Audit log + secret redaction + policy——✓

See PRICING.md. Behavior-changing or data-sharing features (scheduler, work-stealing, telemetry) are off by default and require explicit opt-in. Display-only features (dollar-value savings) default on.

Monorepo layout

PackagePurposePublished
packages/coreMCP server + free packsnpm (MIT)
packages/initnpx @ai-optimizer/init config + savings previewnpm
packages/sdkdefinePack plugin SDKnpm
packages/context-mode-importImport .contextmode configsnpm
packages/proPro/Team packs + scheduler + auditprivate
apps/landing, apps/dashboard, apps/vscode-extension, apps/license-serverSite, local dashboard, IDE widget, license issuanceprivate
tools/pack-benchPack quality benchmark harnessprivate

Architecture details: ARCHITECTURE.md.

Development

npm install            # at repo root (workspaces)
npm run build          # build all packages
npm test               # run all test suites

Security

Local-only by design — license validation is the only network call. Report vulnerabilities per SECURITY.md.

License

Core engine is MIT — see LICENSE. Pro/Team packs are proprietary.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.